iv) Exposure Bias: Similar to language and machine translation models, traditional conversation models are trained with the model input taken from the ground truth rather than a previous output (a method known as teacher forcing [1]}). During inference, however, the model uses past outputs, i.e., is used autoregressively. This is problematic in the dialogue setting since the learning task is unconstrained [2]}. In particular, there are several suitable target responses per dialogue context and vice versa. This discrepancy between training and inference is known as exposure bias [1]}, [4]} and significantly limits the informativeness of the responses as the decoding error compounds rapidly during inference.
